movies_tmdb — The Movies Dataset: TMDB cast, crew and keywords (2017)

Description

Bipartite networks of movies and the people credited in them, and of movies and the keywords describing them, from "The Movies Dataset" assembled by R. Banik and published on Kaggle (version 7, November 2017). The dataset covers the about 45,000 movies listed in the full MovieLens dataset of GroupLens (movies released on or before July 2017); their metadata and credits were collected from the API of The Movie Database (TMDB), a community-edited online movie database. Persons are identified by their TMDB person ids, movies by their TMDB movie ids.

cast: a node is either a movie or an actor, and an edge connects an actor to a movie in whose cast the actor is credited.
crew: a node is either a movie or a crew member (director, writer, producer, composer, editor, etc.), and an edge connects a person to a movie in which the person has a crew credit.
keywords: a node is either a movie or a keyword (a short descriptive tag assigned to movies by TMDB contributors, e.g. "based on novel", "time travel", "duringcreditsstinger"), and an edge connects a keyword to a movie tagged with it.

In cast and crew, each edge is one TMDB credit; a person credited more than once in the same movie (e.g. playing two characters, or as both director and writer) is connected to it by parallel edges. In keywords, each edge is one keyword assignment (there are no parallel edges). Only movies with at least one credit or keyword of the respective kind are included (2,415 of the 45,433 movies with credits have an empty cast list, and 772 an empty crew list; 14,340 of the 45,432 movies in the keyword file have no keywords). Movie metadata is given as in the source (the values of budget, revenue and runtime are 0 when unknown); the TMDB genres of a movie are given in genres.

The networks have the following properties:

All nodes: is_movie, whether the node is a movie (otherwise it is a person, or a keyword in keywords); tmdb_id, the TMDB id of the movie, person or keyword; name, the title of the movie (in English, as in TMDB), the name of the person, or the keyword.
Person nodes (cast and crew only): gender, as coded by TMDB (0: not specified, 1: female, 2: male); -1 for movies.
Movie nodes: original_title; imdb_id, the IMDb identifier; release_date (YYYY-MM-DD) and year, the year of release (-1 if unknown, or for persons and keywords); genres, the list of TMDB genres; original_language (ISO 639-1 code); runtime in minutes; budget and revenue in US dollars; vote_average and vote_count, the average rating (0-10) and number of ratings by TMDB users; popularity, TMDB's popularity score at collection time; adult, whether the movie is flagged as adult content.
cast edges: character, the name of the role; order, the position of the credit in the movie's cast list (0 for the top-billed actor); credit_id, the TMDB credit identifier.
crew edges: job (e.g. Director, Screenplay, Original Music Composer); department (e.g. Directing, Writing, Sound); credit_id, the TMDB credit identifier.

Relation to the hypergraphx movie-cast datasets. The movies-cast-<genre> datasets and the movies-cast-genre-multiplex dataset of the hypergraphx-data repository were built from the same files. To obtain the actors and casts of one genre, take the movie nodes of the cast network whose genres contain it and that have at least two distinct adjacent actors, together with these actors; hypergraphx additionally merges the movies of a genre with exactly the same set of actors into a single group weighted by their number (ignoring repeated credits of an actor in a movie). This reproduces exactly the node and group counts of all 20 hypergraphx genre datasets and of the multiplex dataset (the union of the genre layers). Minor differences: in the source file of movie metadata, 29 movies appear twice (identical rows, or differing only in the popularity score or vote count; the first row is used here), which hypergraphx counts twice in the weights, whereas here each movie appears once; and three rows of that file are broken in two by a formatting error, which hypergraphx reads as rows without title and release date, whereas here they are repaired.

Note that TMDB is community-edited, so the credits, keywords and metadata are of varying completeness. This product uses the TMDB API but is not endorsed or certified by TMDB.

Tags

Economic Employment Affiliation Unweighted Multigraph Metadata

Citation

R. Banik, "The Movies Dataset." Kaggle, version 7 (2017) www.kaggle.com

Upstream URL OK

https://www.kaggle.com/datasets/rounakbanik/the-movies-dataset

Upstream license

Creative Commons CC0 1.0 Universal, https://creativecommons.org/publicdomain/zero/1.0/

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
cast	249,176	562,044	4.51	7.95	24.69	675.54	-0.13	24	0.97	Undirected	Bipartite	is_movie tmdb_id name gender original_title imdb_id release_date year genres original_language runtime budget revenue vote_average vote_count popularity adult	character order credit_id	11.5 MiB	16.3 MiB	15.9 MiB	17.2 MiB
crew	206,236	463,836	4.50	9.16	22.70	694.88	-0.16	28	0.92	Undirected	Bipartite	is_movie tmdb_id name gender original_title imdb_id release_date year genres original_language runtime budget revenue vote_average vote_count popularity adult	job department credit_id	7.8 MiB	10.8 MiB	10.5 MiB	11.7 MiB
keywords	51,048	156,602	6.14	23.60	36.70	26.57	-0.06	14	0.99	Undirected	Bipartite	is_movie tmdb_id name original_title imdb_id release_date year genres original_language runtime budget revenue vote_average vote_count popularity adult		1.6 MiB	2.3 MiB	2.2 MiB	2.3 MiB